• DocumentCode
    231973
  • Title

    A multi-feature fusion based traffic light recognition algorithm for intelligent vehicles

  • Author

    Yue Zhang ; Jianru Xue ; Geng Zhang ; Yingwei Zhang ; Nanning Zheng

  • Author_Institution
    Visual Cognitive Comput. & Intell. Vehicle Lab., Xi´an Jiaotong Univ., Xi´an, China
  • fYear
    2014
  • fDate
    28-30 July 2014
  • Firstpage
    4924
  • Lastpage
    4929
  • Abstract
    Traffic light recognition is a key technology for intelligent vehicles and Advanced Driver Assistance Systems (ADAS). This paper proposes a multi-feature fusion based real-time traffic light recognition algorithm for intelligent vehicles. In the region of interest determined by the vanishing line, technologies including color segmentation, blob detection, and structural feature extraction are employed individually to obtain a set of candidate locations. A fusion algorithm is developed to integrate these results and compute a score for all these possible locations of traffic lights. The score of each candidate denotes its probability of being a traffic light. The final detection is achieved by fusing its score with temporal and geographic information. Extensive experimental results on a real intelligent vehicle show that the proposed algorithm is effective and efficient.
  • Keywords
    feature extraction; image fusion; image segmentation; intelligent transportation systems; object detection; object recognition; traffic engineering computing; ADAS; advanced driver assistance systems; blob detection; color segmentation; geographic information; intelligent vehicles; multifeature fusion; structural feature extraction; temporal information; traffic light recognition algorithm; vanishing line; Brightness; Cameras; Correlation; Image color analysis; Image segmentation; Intelligent vehicles; Shape; Traffic Light; geographic information; intelligent vehicle; multiple features;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control Conference (CCC), 2014 33rd Chinese
  • Conference_Location
    Nanjing
  • Type

    conf

  • DOI
    10.1109/ChiCC.2014.6895775
  • Filename
    6895775